Про Kaggle, карьеру, образование, роль советских ученых в становлении ML и про то, зачем нужен открытый курс по машинному обучению.

Все интервью серии:
- Александр Дьяконов https://youtu.be/qV3yjIyj7Dc
- Константин Воронцов https://youtu.be/DR3mgnEKRgI…

Learning embeddings for classification, retrieval and ranking. - GitHub - facebookresearch/StarSpace: Learning embeddings for classification, retrieval and ranking.

The field of natural language processing is shifting from statistical methods to neural network methods.

There are still many challenging problems to solve in natural language. Nevertheless, deep learning methods are achieving state-of-the-art results…

Official Kaggle Blog ft. interviews from top data science competitors and more!

Torch implementation of various types of GAN (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN, LSGAN) - nashory/gans-collection.torch

Much like the rise of electricity, which started about 100 years ago, AI will revolutionize every major industry. Andrew Ng explains how AI can transform your business, shares major technology trends and thoughts on where your biggest future opportunities…

Tensorflow implementation of Facebook TagSpace. Contribute to flrngel/TagSpace-tensorflow development by creating an account on GitHub.

Привет, Хабр. В своей статье я расскажу вам, что интересного произошло в мире машинного обучения за последний год (в основном в Deep Learning). А произошло очень многое, поэтому я остановился на...

DeepSupervisedHashing - The Deep Supervised Hashing for Image Retrieval on CIFAR10/MNIST/Fashion-MNIST

binge - Recommendation models that use binary rather than floating point operations at prediction time.

Text data requires special preparation before you can start using it for predictive modeling. The text must be parsed to remove words, called tokenization. Then the words need to be encoded as integers or floating point values for use as input to a machine…

You need datasets to practice on when getting started with deep learning for natural language processing tasks. It is better to use small datasets that you can download quickly and do not take too long to fit models. Further, it is also helpful to use standard…

The promise of deep learning in the field of natural language processing is the better performance by models that may require more data but less linguistic expertise to train and operate. There is a lot of hype and large claims around deep learning methods…

Multi-task learning is becoming increasingly popular in NLP but it is still not understood very well which tasks are useful. As inspiration, this post gives an overview of the most common auxiliary tasks used for multi-task learning for NLP.

This post gives an overview of highlights of the Conference on Empirical Methods in Natural Language Processing (EMNLP) 2017 in Copenhagen.

Face detection using deep learning. Contribute to natanielruiz/dockerface development by creating an account on GitHub.

Semantic Segmentation using Fully Convolutional Neural Network. - upul/Semantic_Segmentation

Last year, when I was on a mission to "fix GANs" I had a tendency to focus only
on what the loss function is, and completely disregard the issue of how do we
actually find a minimum. Here is the paper that has finally challenged that
attitude:

* Mescheder…